[1]} proposed two-step fine-tuning of BERT for document-level relation extraction on the DocRED dataset. In the first step, they used BERT to identify whether or not there is a relation between two entities. In the second step, they used BERT to classify the relation. [2]} also used BERT in their model to show that it significantly improved the performance on the DocRED dataset compared to GloVe vectors. [3]} used BERT to identify all possible relations among the entity pairs in documents in a single pass. They used entity types and special tokens to mark all the entity mentions in documents. All entity mentions of an entity received the same special token. Documents were passed to a pre-trained BERT model. An entity mention vector was obtained by averaging the BERT outputs of the entity mention tokens. An entity vector was obtained by averaging all the entity mention vectors of that entity. A bilinear classifier was used to classify the relation between two entities. [4]} proposed a hierarchical inference network for document-level relation extraction. They also showed that using BERT in their model improved performance significantly.
